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    ํ•œ๊ตญ ๊ณ ๋“ฑํ•™์ƒ ์˜์–ด ๋ฐœํ™”์˜ ์žฅ์• ์Œ ๋น„์Œํ™”์™€ ๋ชจ์Œ ์‚ฝ์ž…์— ๋Œ€ํ•œ ์—ฐ๊ตฌ: ๋ชจ๊ตญ์–ด ์ „์ด ํ˜„์ƒ๊ณผ ์˜ค๋ฅ˜ ๋นˆ๋„, ๋ฐœํ™” ๋ช…๋ฃŒ๋„๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์‚ฌ๋ฒ”๋Œ€ํ•™ ์™ธ๊ตญ์–ด๊ต์œก๊ณผ(์˜์–ด์ „๊ณต),2019. 8. ์•ˆํ˜„๊ธฐ.๋ณธ ์—ฐ๊ตฌ๋Š” ํ•œ๊ตญ์ธ ๊ณ ๋“ฑํ•™๊ต ์˜์–ด ํ•™์Šต์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์žฅ์• ์Œ ๋น„์Œํ™”์™€ ๋ชจ์Œ ์‚ฝ์ž… ํ˜„์ƒ์ด ํ•™์Šต์ž๋“ค์˜ ์˜์–ด ๋ฐœํ™”์— ์ „์ด๋˜๋Š” ์ •๋„์™€ ๊ทœ์น™์ด ์ „์ด๋œ ๋ฐœํ™”์˜ ๋ช…๋ฃŒ๋„(intelligibility)๋ฅผ ์‚ดํŽด๋ณด๊ณ ์ž ํ•œ๋‹ค. ์ตœ๊ทผ ์˜์–ด์˜ ์ง€์œ„๊ฐ€ ์˜์–ด๊ถŒ ํ™”์ž๋“ค์˜ ๋ชจ๊ตญ์–ด์—์„œ ๋งŒ๊ตญ ๊ณต์šฉ์–ด๋กœ ๋ฐ”๋€Œ๋ฉด์„œ, ์˜์–ด์˜ ์›์–ด๋ฏผ์˜ ๊ฐœ๋…์ด ํ๋ ค์ง€๊ณ  ์žˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ์˜์–ด ๊ต์œก ํ™˜๊ฒฝ์—์„œ๋„ ์˜ ์–ด ๊ต์œก์˜ ๋ชฉํ‘œ ๋ฐ ๊ธฐ์ค€์œผ๋กœ์„œ ์˜์–ด ์›์–ด๋ฏผ ํ™”์ž๋ฅผ ์ƒ์ •ํ•˜๋Š” ๊ฒƒ์˜ ์‹คํšจ์„ฑ์„ ์žฌ๊ณ ํ•˜๊ณ  ์žˆ๋Š” ์ถ”์„ธ์ด๋‹ค. ์˜์–ด ๋ฐœ์Œ ๊ต์œก์—์„œ๋„ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ, ์˜์–ด ์›์–ด๋ฏผ ํ™” ์ž์™€ ๋™์ผํ•œ ๋ฐœ์Œ ์Šต๋“ํ•˜๋Š” ๊ฒƒ์˜ ์˜๋ฏธ๊ฐ€ ํ๋ ค์ง€๊ณ ์žˆ๋Š” ๊ฐ€์šด๋ฐ, ๊ทธ๋ ‡๋‹ค๋ฉด ๋ชจ ๊ตญ์–ด์˜ ์˜ํ–ฅ์„ ์–ด๋””๊นŒ์ง€ ์ˆ˜์šฉํ•ด์•ผ ํ•˜๋Š”๊ฐ€์— ๋Œ€ํ•œ ์˜๋ฏธ๊ฐ€ ํ•จ๊ป˜ ์ œ๊ธฐ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ช…๋ฃŒํ•œ ๋ฐœํ™”๋ฅผ ํšจ๊ณผ์ ์ธ ์˜์‚ฌ์†Œํ†ต์˜ ํ•„์š”์กฐ๊ฑด์œผ๋กœ ์ œ์‹œํ•˜๋ฉฐ ๋ชจ๊ตญ์–ด์˜ ์˜ํ–ฅ์€ ๋ฐœํ™”์˜ ๋ช…๋ฃŒ๋„๋ฅผ ์ €ํ•ดํ•˜์ง€ ์•Š๋Š” ์„  ๊นŒ์ง€๋งŒ ํ—ˆ์šฉํ•ด์•ผ ํ•  ๊ฒƒ์„ ์ฃผ ์ „์ œ ๋กœ ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์ฐธ์—ฌ์ž์ธ 42๋ช…์˜ ๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ๋“ค์€ ํ•œ๊ตญ์–ด์—์„œ ์žฅ์• ์Œ ๋น„์Œํ™” ๊ทœ์น™์ด ๊ณผ์ž‰ ์ผ๋ฐ˜ํ™”๋˜์–ด ์ ์šฉ๋˜๊ฑฐ๋‚˜, ๋ชจ์Œ ์‚ฝ์ž…์ด ์ผ์–ด๋‚  ๊ฒƒ์œผ๋กœ ์†Œ๋ฆฌ ์—ฐ์†์„ ํฌํ•จํ•œ 20๊ฐœ์˜ ๋‹จ์–ด๋ฅผ ๋ฐ˜๋ณตํ•˜์—ฌ 3๋ฒˆ ์ฝ๋Š” ๊ณผ์—…์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋…น์Œ ์ž๋ฃŒ ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋ถ„์„์„ ํ•œ ๊ฒฐ๊ณผ, ํ•œ๊ตญ์ธ ๊ณ ๋“ฑํ•™์ƒ ์˜์–ด ํ•™์Šต์ž๋“ค์€ ์•ฝ 60.4%์˜ ํ™•๋ฅ ๋กœ ๋ชจ๊ตญ์–ด์˜ ์Œ์ ˆ ๊ตฌ์กฐ์— ์˜ํ–ฅ์„ ๋ฐ›์•„ ๋น„์Œ ์•ž์˜ ์žฅ์• ์Œ์„ ๋น„์Œํ™”ํ•˜๊ฑฐ๋‚˜ ์—ฐ์†๋˜๋Š” ์ž์Œ ์‚ฌ์ด์— ๋ชจ์Œ์„ ์‚ฝ์ž…ํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€๋‹ค. ๋˜ํ•œ, ํ•™์Šต์ž์˜ ์˜์–ด ๋Šฅ์ˆ™๋„๊ฐ€ ๋†’์„์ˆ˜๋ก ๋ชจ๊ตญ์–ด์˜ ์˜ํ–ฅ์„ ์ ๊ฒŒ ๋ฐ›๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•œ๊ตญ์–ด์˜ ์žฅ์• ์Œ ๋น„์Œํ™” ๊ทœ์น™์ด ์ „์ด๋œ ์˜์–ด ๋ฐœํ™”์˜ ๋ช…๋ฃŒ๋„๋ฅผ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ, ์ „์ฒด ๋ฐœํ™” ์ค‘ ์•ฝ 47.42%์˜ ๋ฐœํ™”๋งŒ์ด ๋ณธ ์˜๋„๋Œ€๋กœ ์ „๋‹ฌ๋˜์—ˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ, ๋ฐœ ํ™”์˜ ๋ช…๋ฃŒ๋„๋Š” ์žฅ์• ์Œ์˜ ์กฐ์Œ ์œ„์น˜, ์œ /๋ฌด์„ฑ ์—ฌ๋ถ€, ํ™”์ž์˜ ์˜์–ด ๋Šฅ์ˆ™๋„์— ๋”ฐ ๋ผ ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๋ชจ๊ตญ์–ด์˜ ์˜ํ–ฅ์ด ๋ฐœํ™”์˜ ๋ช…๋ฃŒ๋„๋ฅผ ์ € ํ•ดํ•˜๋Š” ํฐ ์š”์ธ ์ค‘ ํ•˜๋‚˜์ž„์„ ์‹œ์‚ฌํ•˜๋ฉฐ, ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋ณด๋‹ค ์ฒด๊ณ„์ ์ธ ์˜์–ด ๋ฐœ์Œ ๊ต์ˆ˜ ๋ฐ ํ•™์Šต์ด ์ด๋ฃจ์–ด ์งˆ ๊ฒƒ์„ ๊ฒฐ๋ก ๋ถ€์— ์ œ์‹œํ•œ๋‹ค.This study attempted to diagnose Korean high-school EFL learners English pronunciation with particular focus on Korean obstruent nasalization and vowel insertion, in terms of interlanguage transfer, error frequency, and speech intelligibility. In the era where speakers from various mother-tongue backgrounds engage in communication in English, the idea of native became unclear, and consequently aiming for achieving native-like pronunciation continued downtrend. However, this does not mean that the traces of L1 can be excused at all cost. The language learners peculiarity of their mother tongue should be accepted only to the extent that does not hamper the recognition of the individual speech sounds, especially in English as a Lingua Franca (ELF) situation. Considering the nature of information processing procedure between the speakers engaged in ELF communication where the speakers involved in ELF situations do not share a common linguistic background, the possibility of top-down processing is very slim. They not only lack the common pool of sociocultural information that they can utilize to guess the overall direction of the speech, but also to read between the lines when the communication in English breaks down because of cultural implications. The very speech segments that the speakers utter serve as the sole cornerstone that lead to the full understanding of their interaction, and therefore, the communication can only be unhindered only when the speakers correctly produce and understand the individual segment. Starting from this importance of speech accuracy at the segmental level, the study involved 42 Korean high school EFL learners with different English speaking proficiency and recorded their read-alouds of the words containing sound sequences that are expected to be affected by Korean obstruent nasalization rule and vowel epenthesis. Based on the recordings, the frequency and intelligibility of obstruentnasalized speech were measured. The results revealed that 60.4% Korean students are under the influence of obstruent nasalization and vowel epenthesis when speaking in English, while this ratio decreases as the learners English speaking proficiency increases. Also, the intelligibility of obstruent-nasalized speech was 47.52%, which indicates one out of two native speakers of English misunderstood Korean EFL learners speech.TABLE OF CONTENTS ABSTRACT ........................................................................................................... i TABLE OF CONTENTS ................................................................................... iii LIST OF TABLES ............................................................................................... vi LIST OF FIGURES ...........................................................................................vii CHAPTER 1. INTRODUCTION ....................................................................... 1 1.1. The Background of the Study ................................................................. 1 1.2. The Purpose and Significance of the Study ............................................ 2 1.3. Research Questions ................................................................................. 5 1.4. Organization of the Thesis ...................................................................... 5 CHAPTER 2. LITERATURE REVIEW ........................................................... 6 2.1. Influence of L1 on L2 ............................................................................. 6 2.1.1. General Discussions ..................................................................... 6 2.1.2. Previous Studies on L1 Transfer .................................................. 8 2.2. L1 Transfer in L2 Speech: Korean EFL Context .................................. 10 2.2.1. Korean Obstruent Nasalization and Syllable Contact Law........ 10 2.2.2. Vowel insertion .......................................................................... 14 2.3. English as a Lingua Franca (ELF) ........................................................ 15 2.4. Accuracy, Intelligibility and Comprehensibility ................................... 17 2.4.1. Accuracy .................................................................................... 17 2.4.2. Intelligibility and comprehensibility .......................................... 18 CHAPTER 3. METHODOLOGY .................................................................... 20 3.1. Experiment Part 1 ................................................................................. 20 3.1.1. Participants ................................................................................. 20 3.1.2. Materials .................................................................................... 21 3.1.3. Procedure ................................................................................... 23 3.1.4. Data Analysis ............................................................................. 24 3.2. Experiment Part 2 ................................................................................. 25 3.2.1. Scope of Experiment Part 2 ....................................................... 25 3.2.2. Participants ................................................................................. 26 3.2.3. Materials .................................................................................... 26 3.2.4. Procedure ................................................................................... 27 3.2.5. Data Analysis ............................................................................. 28 CHAPTER 4. RESULTS ................................................................................... 30 4.1. Experiment Part 1 ................................................................................. 30 4.1.1. Overall Frequency of Transfer ................................................... 30 4.1.2. Frequency of Transfer across Proficiency Levels ...................... 31 4.2. Experiment Part 2 ................................................................................. 34 4.2.1. Homorganic Sequences .............................................................. 34 4.2.2. Heterorganic Sequences ............................................................. 40 CHAPTER 5. DISCUSSIONS .......................................................................... 49 5.1. L1 Transfer in L2 Speech and Learner Proficiency .............................. 49 5.2 Obstruent Nasalization and Intelligibility .............................................. 51 5.3. Final Remarks and Pedagogical Implications ....................................... 52 CHAPTER 6. CONCLUSION .......................................................................... 54 6.1. Summary of the Thesis ......................................................................... 54 6.1.1. English proficiency and Frequency of Rule Transfer ................ 55 6.1.2. Intelligibility of obstruent nasalized English speech samples ... 56 6.2. Limitations and Suggestions for Future Research ................................ 57 REFERENCES ................................................................................................... 59 APPENDICES .................................................................................................... 64 ๊ตญ ๋ฌธ ์ดˆ ๋ก ......................................................................................................... 69Maste

    ๊ฐ€์‹œ๊ด‘ ํ†ต์‹ ์˜ CSK ๋ณ€์กฐ๊ธฐ๋ฒ•์—์„œ ํ”Œ๋ฆฌ์ปค ์™„ํ™” ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2015. 8. ์ด์ •์šฐ.์ตœ๊ทผ IoT (Internet of Things)๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๋กœ ๊ฐ€์‹œ๊ด‘ ํ†ต์‹ ์ด ์ฃผ๋ชฉ์„ ๋ฐ›๊ณ  ์žˆ๋‹ค. ๊ฐ€์‹œ๊ด‘ ํ†ต์‹ ์€ radio frequency ์˜์—ญ์ธ 3kHz์—์„œ 300GHz์˜ ์ฃผํŒŒ์ˆ˜ ๋Œ€์—ญ์„ ์‚ฌ์šฉํ•˜๋Š” ๊ธฐ์กด์˜ ํ†ต์‹  ๋ฐฉ์‹๊ณผ ๋‹ฌ๋ฆฌ ๊ฐ€์‹œ๊ด‘์„ ์˜์—ญ(380nm~ 780nm)์„ ๋ฐ์ดํ„ฐ ์ „์†ก์— ์‚ฌ์šฉํ•œ๋‹ค. ๊ฐ€์‹œ๊ด‘์„ ์„ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ํ†ต์‹ ์˜ ๊ธฐ๋Šฅ์€ ๋ฌผ๋ก  ์กฐ๋ช…์˜ ๊ธฐ๋Šฅ์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์กฐ๋ช…์˜ ๊ธฐ๋Šฅ๊ณผ ๊ด€๋ จ๋œ ๊ธฐ์ˆ ์ด ๋งŽ์ด ์—ฐ๊ตฌ๋˜์–ด ์™”๋‹ค. ์˜ˆ๋ฅผ ๋“ค๋ฉด ๋ฐ์ดํ„ฐ ์ „์†ก์„ ํ•˜๋ฉด์„œ ์กฐ๋ช…์˜ ๋ฐ๊ธฐ๋ฅผ ์กฐ์ ˆ์„ ํ•˜๋Š” ๊ธฐ์ˆ ์ด๋‚˜ ์กฐ๋ช…์˜ ํ’ˆ์งˆ์„ ์ข‹๊ฒŒ ํ•˜๋Š” ๊ธฐ์ˆ ์ด ๊ทธ๊ฒƒ์ด๋‹ค. ํŠนํžˆ ๋ฐ์ดํ„ฐ๋ฅผ ์ „์†กํ•จ์— ์žˆ์–ด์„œ ์‚ฌ๋žŒ์˜ ๋ˆˆ์ด ์ธ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ์กฐ๋ช…์˜ ๋ฐ๊ธฐ๋ณ€ํ™”๋ฅผ ํ”Œ๋ฆฌ์ปค๋ผ๊ณ  ํ•˜๋Š”๋ฐ ์ด๋ฅผ ์™„ํ™”ํ•˜๊ธฐ ์œ„ํ•œ ๊ธฐ์ˆ ์ด ์—ฐ๊ตฌ๋˜์–ด ์™”๋‹ค. ๊ธฐ์กด์— ์—ฐ๊ตฌ๋œ ํ”Œ๋ฆฌ์ปค ์™„ํ™” ๊ธฐ์ˆ ์€ OOK (On-Off Keying)๋ณ€์กฐ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•  ๋•Œ ํ”Œ๋ฆฌ์ปค๋ฅผ ์™„ํ™”ํ•˜๋Š” ๊ธฐ์ˆ ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” CSK (Color-Shift Keying)๋ณ€์กฐ๊ธฐ๋ฒ•์—์„œ ํ”Œ๋ฆฌ์ปค ์™„ํ™” ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. CSK ๋ณ€์กฐ๊ธฐ๋ฒ•์€ ์กฐ๋ช…์˜ ๋ฐ๊ธฐ๋Š” ์ผ์ •ํ•˜์ง€๋งŒ, color variation์ด ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ color variation๋„ ํ”Œ๋ฆฌ์ปค์˜ ์ผ์ข…์œผ๋กœ ๊ณ ๋ คํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ์กด์— OOK ๋ณ€์กฐ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•  ๋•Œ ํ”Œ๋ฆฌ์ปค๋ฅผ ์™„ํ™”ํ•˜๋Š” ๊ธฐ์ˆ ์ธ line code๊ธฐ๋ฒ•์—์„œ ์•„์ด๋””์–ด๋ฅผ ์–ป์–ด red, green, blue์˜ LED๋ฅผ ์‚ฌ์šฉํ•˜๋Š” CSK๋ณ€์กฐ๊ธฐ๋ฒ•์—์„œ red, green, blue์˜ ์กฐํ•ฉ์œผ๋กœ color balance๋ฅผ ๋งž์ถœ ์ˆ˜ ์žˆ๋Š” codebook์„ ์ œ์•ˆํ•˜์˜€๋‹ค. red, green, blue์˜ ์ˆ˜๊ฐ€ ๊ท ์ผํ•œ codeword๋ฅผ ๋งŒ๋“ค๊ณ  ๊ทธ ์ค‘ ์ „์†ก์— ์‚ฌ์šฉํ•  codebook์„ ์„ ํƒํ•˜์˜€๋‹ค. Codebook ์„ ํƒ ์‹œ ๊ณ ๋ ค๋˜์–ด์•ผ ํ•  ์ ์€ codebook์˜ MD (Minimum Distance)์ด๋‹ค. MD๊ฐ€ ํด์ˆ˜๋ก ๋””์ฝ”๋”ฉ์‹œ ์—๋Ÿฌ ๋ฐœ์ƒํ™•๋ฅ ์ด ์ค„์–ด๋“ค๊ฒŒ ๋œ๋‹ค. ๋”ฐ๋ผ์„œ MD๋ฅผ ์ตœ๋Œ€ํ™” ํ•˜๋Š” codebook์„ ์„ ํƒํ•˜์˜€๋‹ค. Matlab simulation์„ ํ†ตํ•ด ๊ธฐ์กด์˜ CSK๋ฐฉ์‹๊ณผ ์ œ์•ˆํ•˜๋Š” ๊ธฐ๋ฒ•์„ ์ด์šฉํ•œ CSK๋ฐฉ์‹์˜ ํ”Œ๋ฆฌ์ปค์˜ ์ •๋„๋ฅผ ๋น„๊ตํ•˜์˜€๋‹ค. ํ”Œ๋ฆฌ์ปค์˜ ์ •๋„๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ์ฒ™๋„๋กœ red, green, blue์˜ ๋ฐ๊ธฐ๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ๋ฒกํ„ฐ์— ๋Œ€ํ•ด moving average๋ฅผ ๊ตฌํ•ด์„œ ๋ฐฑ์ƒ‰๊ด‘์„ ๋‚˜ํƒ€๋‚ด๋Š” ๋ฒกํ„ฐ์™€์˜ ๊ฑฐ๋ฆฌ๋ฅผ ์ธก์ •ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋‘ ๋ฐฉ์‹์—์„œ SNR์— ๋”ฐ๋ฅธ BER์„ ๋น„๊ตํ•˜๊ณ  ๋ถ„์„ํ•˜์˜€๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  6 1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 6 1.2 ์—ฐ๊ตฌ์˜ ๋‚ด์šฉ 7 ์ œ 2 ์žฅ ๊ฐ€์‹œ๊ด‘ ํ†ต์‹  8 2.1 ์†ก์‹ ๊ธฐ์™€ ์ˆ˜์‹ ๊ธฐ 8 2.2 System Model 9 ์ œ 3 ์žฅ ํ”Œ๋ฆฌ์ปค ํ˜„์ƒ 11 3.1 ํ”Œ๋ฆฌ์ปค ๋ฐœ์ƒ์š”์ธ 11 3.2 ํ”Œ๋ฆฌ์ปค ์™„ํ™”๊ธฐ๋ฒ• 11 3.2.1 ํ”„๋ ˆ์ž„ ๋‚ด๋ถ€์—์„œ์˜ ํ”Œ๋ฆฌ์ปค 11 ์ œ 4 ์žฅ CSK ๋ณ€์กฐ๊ธฐ๋ฒ• 16 4.1 CSK Constellation Design 16 4.1.1 Chromaticity gamut 16 4.1.2 CSK Constellation Design 18 ์ œ 5 ์žฅ CSK ๋ณ€์กฐ๋ฐฉ์‹์—์„œ ํ”Œ๋ฆฌ์ปค ์™„ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜ 19 5.1 ์ œ์•ˆ๋˜๋Š” ์ฝ”๋”ฉ ๊ธฐ๋ฒ• 19 5.1.1 4 Symbol Mapping 19 5.1.2 16 Symbol Mapping 20 ์ œ 6 ์žฅ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 26 ์ œ 7 ์žฅ ๊ฒฐ๋ก  30 ์ฐธ๊ณ  ๋ฌธํ—Œ 31 Abstract 33Maste

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์Œ์•…ํ•™๊ณผ ์ด๋ก ์ „๊ณต,2002.Maste

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์Œ์•…๊ณผ, 2015. 2. ์ด๊ฒฝ์„ .์ธ๊ฐ„์˜ ์‚ถ์— ๊ผญ ํ•„์š”ํ•œ ๋ฌธํ™”์— ์žˆ์–ด์„œ ์ด๋ฅผ ํ‘œํ˜„ํ•˜๊ธฐ ์œ„ํ•œ ์žฅ๋ฅด์ธ ์Œ์•…๊ณผ ๋ฏธ์ˆ ์€ ์ƒํ˜ธ ๋ณด์™„์ž‘์šฉํ•˜๋ฉฐ ์„œ๋กœ ๊ณต์กดํ•˜๋Š” ๊ฐœ๋…์ด๋‹ค. ์Œ์•…๊ณผ ๋ฏธ์ˆ ์€ ํ‘œํ˜„๋ฐฉ์‹์˜ ์ธก๋ฉด์—์„œ ๊ฐ๊ธฐ ์ฐจ์ด์ ์ด ์žˆ์œผ๋‚˜ ์˜ˆ์ˆ ์ด๋ผ๋Š” ํ•˜๋‚˜์˜ ํฐ ๋ถ„๋ฅ˜ ์•„๋ž˜ ํ•จ๊ป˜ ๊ณต์กดํ•œ๋‹ค๋Š” ์ ์—์„œ ์ด ๋‘ ๊ฐ€์ง€๋ฅผ ํ•˜๋‚˜์˜ ๊ณตํ†ต์  ํ˜•์‹์œผ๋กœ ๋น„๊ต ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์€ ์˜ˆ์ˆ ์ธ์œผ๋กœ์„œ ํฅ๋ฏธ๋กœ์šด ๋„์ „์ด๋ผ ์ƒ๊ฐํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์Œ์•… ํ˜•์‹์ธ ๋‘ ๋ถ€๋ถ„ ํ˜•์‹, ์„ธ ๋ถ€๋ถ„ ํ˜•์‹, ๋ณ€์ฃผ๊ณก ํ˜•์‹๊ณผ ๋ก ๋„ ํ˜•์‹์— ๋Œ€ํ•˜์—ฌ ๋ฏธ์ˆ  ํšŒํ™” ์ž‘ํ’ˆ์—์˜ ์ ์šฉ ๋ฐ ๋ถ„์„์œผ๋กœ์„œ, ๊ฐ ์Œ์•…์  ํ˜•์‹์„ ๋ฏธ์ˆ ์— ์ ์šฉํ•จ์œผ๋กœ์„œ ์–‘์ž๊ฐ„์˜ ์œ ๊ธฐ์  ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•ด๋ณด๊ณ ์ž ํ•œ๋‹ค. ์ดํ•˜ ์ˆœ์„œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ๋จผ์ € ์ œ 2 ์žฅ์—์„œ๋Š” 2 ๋ถ€๋ถ„ ํ˜•์‹์— ๋Œ€ํ•œ ์Œ์•…์  ๊ฐœ์š”์™€ ์ ์ ˆํ•œ ์•…๊ณก ์˜ˆ์‹œ๋ฅผ ๋“ค์–ด ์ด์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ๋•๊ณ , 2 ๋ถ€๋ถ„ ํ˜•์‹์€ AA ํ˜•์‹๊ณผ AB ํ˜•์‹์œผ๋กœ ๋‚˜๋ˆ„์–ด ๋ฏธ์ˆ ์ž‘ํ’ˆ์—์„œ ์ด๊ฐ€ ์–ด๋–ป๊ฒŒ ๋ถ„์„๋˜๋Š”์ง€๋ฅผ ์•Œ์•„๋ณด๊ณ ์ž ํ•œ๋‹ค. ๋‹ค์Œ์˜ ์ œ 3 ์žฅ์—์„œ๋Š” 3 ๋ถ€๋ถ„ ํ˜•์‹์— ๋Œ€ํ•˜์—ฌ ์Œ์•…์  ๊ฐœ์š”์™€ ํ•จ๊ป˜ ์•…๊ณก ์˜ˆ์‹œ๋ฅผ ๋“ค์–ด ์„ค๋ช…ํ•˜๊ณ , ๋‹ค์–‘ํ•œ ๋ฏธ์ˆ ์ž‘ํ’ˆ๋“ค์„ ๋ถ„์„ํ•˜์—ฌ ์Œ์•…์˜ 3 ๋ถ€๋ถ„ ํ˜•์‹์— ์ ์šฉํ•˜์—ฌ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ์ œ 4 ์žฅ์€ ๋ณ€์ฃผ๊ณก ํ˜•์‹์— ๋Œ€ํ•˜์—ฌ ์‹œ๋Œ€์  ํ๋ฆ„์„ ๋ฐ˜์˜ํ•˜์—ฌ ์ „๊ธฐ ๋ณ€์ฃผ๊ณก ํ˜•์‹๊ณผ ํ›„๊ธฐ ๋ณ€์ฃผ๊ณก ํ˜•์‹์ธ ๋‘ ๋ถ„๋ฅ˜ ๋‚˜๋ˆ„์–ด ์ •์˜ํ•œ ํ›„, ์ด์— ๋Œ€ํ•œ ๋Œ€ํ•œ ์Œ์•…์  ์ •์˜์™€ ์ ์ ˆํ•œ ์Œ์•… ์•…๊ณก์˜ ์˜ˆ์‹œ๋ฅผ ๋“ค์–ด ์šฉ์–ด๋ฅผ ์„ค๋ช…ํ•˜๊ณ  ์ด๋Ÿฌํ•œ ๊ฐœ๋…์ด ๋ฏธ์ˆ  ์ž‘ํ’ˆ์—์„œ ๋ฐœํ˜„๋œ ์ ์ ˆํ•œ ์ž‘ํ’ˆ๋“ค์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ œ 5 ์žฅ์€ ๋ก ๋„ ํ˜•์‹์— ๋Œ€ํ•œ ๋ถ„์„์œผ๋กœ์„œ, ์•ž์„œ ์—ฐ๊ตฌํ–ˆ๋˜ ๋ฐฉ๋ฒ•๊ณผ ๊ฐ™์ด ํ˜•์‹์˜ ๊ธฐ์›๊ณผ ํ•จ๊ป˜ ์šฉ์–ด์˜ ๊ฐœ๋…์„ ์„ค๋ช…ํ•˜๊ณ  ์Œ์•… ์•…๊ณก์œผ๋กœ ์„ค๋ช…ํ•˜์˜€์œผ๋ฉฐ ์ด๋ฅผ ๋ฏธ์ˆ  ์ž‘ํ’ˆ์— ๋Œ€์ž…ํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋Œ€๋ถ€๋ถ„์˜ ์Œ์•… ์ž‘ํ’ˆ๋“ค์€ ์ž‘๊ณก๊ฐ€๊ฐ€ ๋ฏธ๋ฆฌ ์ •ํ•ด์ง„ ํ‹€ ์†์— ์Œํ‘œ๋“ค์„ ์ ์–ด๋‚ด๋ฆผ์œผ๋กœ์„œ ์ž‘๊ณก๋˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ, ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์ž‘๊ณก๊ฐ€๊ฐ€ ํ‘œํ˜„ํ•˜๊ณ ์ž ํ•˜๋Š” ์Œ์•…์„ ๊ทธ์˜ ์ƒ๊ฐ ์•ˆ์—์„œ๋ถ€ํ„ฐ ๋„์ถœํ•ด๋‚ด๋Š” ๊ณผ์ •์—์„œ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ์Œ์•…์  ํ‹€์ธ ์Œ์•… ํ˜•์‹์„ ์‚ฌ์šฉํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด์™€ ๊ฐ™์ด ๋ฏธ์ˆ  ์ž‘ํ’ˆ ๋˜ํ•œ ํ™”๊ฐ€๋“ค์ด ๊ทธ๋“ค์˜ ์ž‘ํ’ˆ์„ ์ฐฝ์กฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ๊ทธ๋“ค์˜ ์ƒ๊ฐ์„ ๋„์ถœํ•ด๋‚ด๋Š” ๊ณผ์ •์—์„œ ์ „์ฒด์ ์ธ ํ‹€์„ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ๋Š” ๊ฒƒ์ธ๋ฐ, ์ด๊ฐ€ ์Œ์•… ํ˜•์‹๊ณผ ๊ฐ™์ด ์ •์‹ ๋ช…์นญ์˜ ํ˜•์‹์œผ๋กœ ๋ถ„๋ฅ˜๋˜์ง€ ์•Š๋Š”๋‹ค๋Š” ์ ์„ ๊ฐ์•ˆํ•  ๋•Œ ์ด๋Š” ์Œ์•…๊ณผ ๋ฏธ์ˆ ์˜ ์ฐฝ์กฐ์  ์‚ฐ๋ฌผ์— ๋Œ€ํ•œ ๋ฐœ์ „๋‹จ๊ณ„์˜ ๊ณตํ†ต์  ํŠน์„ฑ์ด๋ผ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ํ˜„์žฌ ์Œ์•…๊ณผ ๋ฏธ์ˆ ์—์„œ ๊ฐ ๋ถ„์•ผ์˜ ํ‘œํ˜„ ๋ฐฉ์‹์ด๋‚˜ ๊ธฐ๋ฒ• ๋“ฑ์„ ๋‹ด์€ ์—ฐ๊ตฌ๋Š” ๋งŽ์ด ์กด์žฌํ•˜์ง€๋งŒ ์Œ์•…์˜ ํ˜•์‹์— ๋ฏธ์ˆ ์„ ๋Œ€์ž…ํ•˜์—ฌ ๋ถ„์„ํ•˜๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์—†๋‹ค. ์ด๋Ÿฌํ•œ ์ธก๋ฉด์—์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์˜๋ฏธ์žˆ๋Š” ์ž‘์—…์ด ๋  ๊ฒƒ์ด๋ฉฐ ๋‚˜์•„๊ฐ€ ์Œ์•…์ธ๋“ค์—๊ฒŒ๋„ ์–‘์ž ๊ฐ„์˜ ์˜ˆ์ˆ ์  ๊ด€๊ณ„๋ฅผ ์ดํ•ดํ•˜๋Š” ์‹œ๊ฐ์— ๋งŽ์€ ๋„์›€์„ ์ค„ ๊ฒƒ์ด๋ผ ํ™•์‹ ํ•œ๋‹ค.๊ตญ๋ฌธ ์ดˆ๋ก โ… . ์„œ๋ก โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆ1 โ…ก. 2 ๋ถ€๋ถ„ ํ˜•์‹ 1. ํ˜•์‹์˜ ๊ฐœ์š”์™€ ์Œ์•… ์ž‘ํ’ˆ์˜ ์˜ˆ์‹œโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ........4 2. ๋ฏธ์ˆ  ์ž‘ํ’ˆ์—์˜ ์ ์šฉโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ......โ€ฆ10 โ…ข. 3 ๋ถ€๋ถ„ ํ˜•์‹ 1. ํ˜•์‹์˜ ๊ฐœ์š”์™€ ์Œ์•… ์ž‘ํ’ˆ์˜ ์˜ˆ์‹œโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.........21 2. ๋ฏธ์ˆ  ์ž‘ํ’ˆ์—์˜ ์ ์šฉโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ......29 โ…ฃ. ๋ณ€์ฃผ๊ณก ํ˜•์‹ 1. ํ˜•์‹์˜ ๊ฐœ์š”์™€ ์Œ์•… ์ž‘ํ’ˆ์˜ ์˜ˆ์‹œโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.........42 2. ๋ฏธ์ˆ  ์ž‘ํ’ˆ์—์˜ ์ ์šฉโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.........47 โ…ค. ๋ก ๋„ ํ˜•์‹ 1. ํ˜•์‹์˜ ๊ฐœ์š”์™€ ์Œ์•… ์ž‘ํ’ˆ์˜ ์˜ˆ์‹œโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.........72 2. ๋ฏธ์ˆ  ์ž‘ํ’ˆ์—์˜ ์ ์šฉโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.........78 โ…ฅ. ๊ฒฐ๋ก โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ....87 โ…ฆ. ๋„ํŒ ๋ชฉ๋กโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ89 ์ฐธ๊ณ ๋ฌธํ—Œโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.95 AbstractMaste

    ๊ธฐ์ˆ ์ง„๋ณด์™€ ์ฒญ๋…„๊ณ ์šฉ

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    ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธฐ์ˆ ๋ฐœ์ „์ด ์ฒญ๋…„๊ณ ์šฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด ๋…ธ๋™์ž๋ฅผ ์ฒญ๋…„์ธต๊ณผ ์ค‘์žฅ๋…„์ธต์œผ๋กœ ๋‚˜๋ˆˆ ํ›„ ๊ธฐ์ˆ ๋ฐœ์ „์ด ๊ธฐ์—…์˜ ๋…ธ๋™์ˆ˜์š”์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ์—ฐ๋ น๋ณ„๋กœ ์ฐจ๋ณ„ํ™”๋˜์—ˆ๋Š”์ง€๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ธฐ์ˆ ๋ฐœ์ „์€ ์ž๋ณธ์˜ ํšจ์œจ์„ฑ์„ ๋ณ€๋™์‹œ์ผœ ๊ธฐ์—…์˜ ๋…ธ๋™์ˆ˜์š”์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”๋ฐ, ๊ทธ ํšจ๊ณผ๋Š” ๋…ธ๋™๊ณผ ์ž๋ณธ์˜ ๋Œ€์ฒด์„ฑ์— ๋”ฐ๋ผ ํฌ๊ธฐ๋‚˜ ๋ฐฉํ–ฅ์„ฑ์ด ๋‹ฌ๋ผ์ง€๋ฏ€๋กœ ๊ฐ ์—ฐ๋ น์ธต ๋…ธ๋™์ž์˜ ์ž๋ณธ๊ณผ์˜ ๋Œ€์ฒดํƒ„๋ ฅ์„ฑ์„ ์ˆ˜์ •ํ•˜์—ฌ ๋น„๊ตํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ํ†ต๊ณ„์ฒญ ๊ด‘์—…์ œ์กฐ์—…์กฐ์‚ฌ์™€ ๊ณ ์šฉํ˜•ํƒœ๋ณ„ ๊ทผ๋กœ์‹คํƒœ์กฐ์‚ฌ ์ž๋ฃŒ(2000๋…„-2014๋…„)๋ฅผ ํ™œ์šฉ, ์‚ฐ์—…๊ฐ„ ์ฐจ๋ฆฌ๋ฅผ ์‹๋ณ„์— ์ด์šฉํ•˜์—ฌ ์ƒ์‚ฐํ•จ์ˆ˜์˜ ์ฃผ์š” ๋ชจ์ˆ˜๋ฅผ ์ถ”์ •ํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, ์ฒญ๋…„๋…ธ๋™์ž๋Š” ์ค‘์žฅ๋…„๋…ธ๋™์ž์— ๋น„ํ•ด ์ƒ๋Œ€์ ์œผ๋กœ ์ž๋ณธ๊ณผ์˜ ๋Œ€์ฒดํƒ„๋ ฅ์„ฑ์ด ํฐ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‹ค๋งŒ ์—ฐ๋ น๋ณ„ ์ž๋ณธ ๋Œ€์ฒดํƒ„๋ ฅ์„ฑ ์ฐจ์ด๊ฐ€ ํฌ์ง€ ์•Š์€ ์ ์„ ๊ฐ์•ˆํ•  ๋•Œ ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์ตœ์†Œํ•œ ๊ธฐ์ˆ ๋ฐœ์ „์ด ์ฒญ๋…„์ธต ๋…ธ๋™์ˆ˜์š”์— ์ƒ๋Œ€์ ์œผ๋กœ ๊ธ์ •์  ํšจ๊ณผ๋ฅผ ์ฃผ์ง€๋Š” ๋ชปํ•˜๋Š” ๊ฒƒ์œผ๋กœ ํ•ด์„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋„๊ตฌ๋ณ€์ˆ˜๋ฅผ ํ™œ์šฉํ•œ ์ถ”์ •, ๊ต์œก์ˆ˜์ค€, ๋‹ค์–‘ํ•œ ์—ฐ๋ น๊ธฐ์ค€์˜ ์ ์šฉ, ์ง์ข…๋ณ„ ์ฐจ์ด ๋“ฑ์„ ๊ณ ๋ คํ•œ ๊ฐ•๊ฑด์„ฑ ๋ถ„์„์—์„œ๋„ ๊ฒฐ๊ณผ๋Š” ํฌ๊ฒŒ ๋‹ค๋ฅด์ง€ ์•Š์•˜๋”ฐ. ์ด๋Ÿฌํ•œ ๋ถ„์„๊ฒฐ๊ณผ๋Š” ๊ธฐ์ˆ ์ง„๋ณด๊ฐ€ ์ด๋ฏธ ๊ฒฝํ—˜์ด ์ถ•์ ๋œ ์ค‘์žฅ๋…„์ธต ๊ณ ์šฉ์— ๋น„ํ•ด ์ƒˆ๋กญ๊ฒŒ ๋…ธ๋™์‹œ์žฅ์— ์ง„์ž…ํ•˜๋Š” ์ฒญ๋…„์ธต ๊ณ ์šฉ์— ๋” ํฐ ๋ถ€์ •์  ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ์œผ๋ฉฐ ์ฒญ๋…„๊ณ ์šฉ์ •์ฑ…์ด ๊ธฐ์ˆ ๋ฐœ์ „์œผ๋กœ ์ธํ•œ ๋…ธ๋™์ˆ˜์š”์˜ ๊ตฌ์กฐ์  ๋ณ€ํ™”๋ฅผ ๊ณ ๋ คํ•ด์„œ ์ถ”์ง„๋˜์–ด์•ผ ํ•จ์„ ์‹œ์‚ฌํ•œ๋‹ค.TRU

    Technological Progress and Youth Employment

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    ๋ณธ ๋…ผ๋ฌธ์€ ๊ธฐ์ˆ ๋ฐœ์ „์ด ์ฒญ๋…„๊ณ ์šฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋…ธ๋™์ž๋ฅผ ์ฒญ๋…„์ธต๊ณผ ์ค‘์žฅ๋…„์ธต์œผ๋กœ ๋‚˜๋ˆˆ ํ›„ ๊ธฐ์ˆ ๋ฐœ์ „์ด ๊ธฐ์—…์˜ ๋…ธ๋™์ˆ˜์š”์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ์—ฐ๋ น๋ณ„๋กœ ์ฐจ๋ณ„ํ™”๋˜์—ˆ๋Š”์ง€๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ธฐ์ˆ ๋ฐœ์ „์€ ์ž๋ณธ ์˜ ํšจ์œจ์„ฑ์„ ๋ณ€๋™์‹œ์ผœ ๊ธฐ์—…์˜ ๋…ธ๋™์ˆ˜์š”์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”๋ฐ, ๊ทธ ํšจ๊ณผ๋Š” ๋…ธ ๋™๊ณผ ์ž๋ณธ์˜ ๋Œ€์ฒด์„ฑ์— ๋”ฐ๋ผ ํฌ๊ธฐ๋‚˜ ๋ฐฉํ–ฅ์„ฑ์ด ๋‹ฌ๋ผ์ง€๋ฏ€๋กœ ๊ฐ ์—ฐ๋ น์ธต ๋…ธ๋™์ž ์˜ ์ž๋ณธ๊ณผ์˜ ๋Œ€์ฒดํƒ„๋ ฅ์„ฑ์„ ์ถ”์ •ํ•˜์—ฌ ๋น„๊ตํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ํ†ต๊ณ„์ฒญ ๊ด‘์—…์ œ ์กฐ์—…์กฐ์‚ฌ์™€ ๊ณ ์šฉํ˜•ํƒœ๋ณ„ ๊ทผ๋กœ์‹œ๊ฐ„์กฐ์‚ฌ ์ž๋ฃŒ(2000๋…„~2014๋…„)๋ฅผ ํ™œ์šฉ, ์‚ฐ์—… ๊ฐ„ ์ฐจ์ด๋ฅผ ์‹๋ณ„์— ์ด์šฉํ•˜์—ฌ ์ƒ์‚ฐํ•จ์ˆ˜์˜ ์ฃผ์š” ๋ชจ์ˆ˜๋ฅผ ์ถ”์ •ํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ ๊ณผ, ์ฒญ๋…„๋…ธ๋™์ž๋Š” ์ค‘์žฅ๋…„๋…ธ๋™์ž์— ๋น„ํ•ด ์ƒ๋Œ€์ ์œผ๋กœ ์ž๋ณธ๊ณผ์˜ ๋Œ€์ฒดํƒ„๋ ฅ์„ฑ ์ด ํฐ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‹ค๋งŒ ์—ฐ๋ น๋ณ„ ์ž๋ณธ ๋Œ€์ฒดํƒ„๋ ฅ์„ฑ ์ฐจ์ด๊ฐ€ ํฌ์ง€ ์•Š์€ ์  ์„ ๊ฐ์•ˆํ•  ๋•Œ ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์ตœ์†Œํ•œ ๊ธฐ์ˆ ๋ฐœ์ „์ด ์ฒญ๋…„์ธต ๋…ธ๋™์ˆ˜์š”์— ์ƒ๋Œ€ ์ ์œผ๋กœ ๊ธ์ •์  ํšจ๊ณผ๋ฅผ ์ฃผ์ง€๋Š” ๋ชปํ•˜๋Š” ๊ฒƒ์œผ๋กœ ํ•ด์„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋„๊ตฌ๋ณ€์ˆ˜๋ฅผ ํ™œ์šฉํ•œ ์ถ”์ •, ๊ต์œก์ˆ˜์ค€, ๋‹ค์–‘ํ•œ ์—ฐ๋ น๊ธฐ์ค€์˜ ์ ์šฉ, ์ง์ข…๋ณ„ ์ฐจ์ด ๋“ฑ์„ ๊ณ ๋ คํ•œ ๊ฐ•๊ฑด์„ฑ ๋ถ„์„์—์„œ๋„ ๊ฒฐ๊ณผ๋Š” ํฌ๊ฒŒ ๋‹ค๋ฅด์ง€ ์•Š์•˜๋‹ค. ์ด๋Ÿฌํ•œ ๋ถ„์„๊ฒฐ๊ณผ๋Š” ๊ธฐ์ˆ ๋ฐœ ์ „์ด ์ค‘์žฅ๋…„๊ณ ์šฉ์— ๋น„ํ•ด ์ฒญ๋…„๊ณ ์šฉ์˜ ์ฆ๋Œ€๋ฅผ ๊ฐ€์ ธ์˜ค๊ธฐ ์‰ฝ์ง€ ์•Š์œผ๋ฉฐ ์ฒญ๋…„๊ณ  ์šฉ์ •์ฑ…์ด ๊ธฐ์ˆ ๋ฐœ์ „์œผ๋กœ ์ธํ•œ ๋…ธ๋™์ˆ˜์š”์˜ ๊ตฌ์กฐ์  ๋ณ€ํ™”๋ฅผ ๊ณ ๋ คํ•ด์„œ ์ถ”์ง„๋˜ ์–ด์•ผ ํ•จ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์ทจ์—…๋ณด์กฐ๊ธˆ ๋“ฑ ๋‹จ์ˆœํ•œ ์ฒญ๋…„๊ณ ์šฉ ์žฅ๋ ค์ •์ฑ…๋ณด๋‹ค ์ƒˆ๋กœ์šด ๊ธฐ์ˆ ์„ ์ฒญ๋…„๋…ธ๋™์ž๋“ค์ด ํšจ์œจ์ ์œผ๋กœ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•˜๋Š” ์ง์—…๊ต์œก ๊ฐ•ํ™” ๋“ฑ์ด ์ฒญ๋…„๊ณ ์šฉ ๋ฌธ์ œ ๊ฐœ์„ ์„ ์œ„ํ•ด ๋”์šฑ ํšจ๊ณผ์ ์ผ ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. This paper analyzes the extent to which technology progress and youth employment are related. In doing so, we divide workers into two groups - young workers and elder (prime-aged) workers - and study how differently technology progress affects demand for different groups of workers. In particular, we estimate elasticity of substitution between physical capital and workers ร  la Jaimovich et al. (2013) since demand for different groups of labor crucially depends on the elasticity. By using Korean labor market data between 2000 and 2014, we estimate key parameters of the production function by utilizing industry variation observed in the data. Our findings robustly indicate that the elasticity of substitution is greater (or at least not smaller) for young workers than for elder workers. This finding is robust to instrumental variable regression and to different sub-groups of workers, including educational attainment, different criteria for young/prime-aged workers, male workers, occupational groups, and size of the firms. Our findings suggest that policies that help young workers to obtain skills complement to new technology can be more efficient than policies that do not consider structural change of the labor market.I. ์„œ๋ก  II. ์‹ค์ฆ๋ถ„์„ ๋ฐฉ๋ฒ•๋ก  ๋ฐ ๋ณ€์ˆ˜ ์†Œ๊ฐœ III. ๋ถ„์„ ๊ฒฐ๊ณผ IV. ๊ฒฐ๋ก  ๋ฐ ์ •์ฑ…์  ์‹œ์‚ฌ์  ์ฐธ๊ณ ๋ฌธํ—Œ ๋ถ€
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